Abstract: This study investigates the resilience of freight operations during the Durkee Fire in Oregon, using telematics data from heavy commercial vehicles (HCVs) to develop the Speed Consistency Resilience Index (SCRI). The analysis leverages trip-level data, including vehicle speeds, travel times, and distances, to assess performance across three phases of the fire: before, during, and after the event. The research highlights the vulnerability of freight systems to disruptions such as wildfires, which cause delays, rerouting, and increased operational costs, emphasizing the urgent need for methods to quantify resilience in freight networks. The methodology integrates statistical testing and spatial clustering to assess the fire’s impact. Key findings reveal significant disruptions in speed consistency, particularly along I-84, where closures led to sharp declines in resilience, especially in Baker County. In contrast, US-20 remained more stable, offering a viable alternative route with less disruption. The SCRI metric, which quantifies the consistency of truck speeds during disruptions, provided granular insights into spatial and temporal variations in resilience across the region. It offers a novel framework for understanding and improving the resilience of freight operations. These findings have practical implications for logistics operators and policymakers, helping to enhance contingency planning and inform infrastructure resilience strategies.

Rakan Mohammad Albatayneh is a Civil Engineering graduate student at Oregon State University, where he is pursuing a PhD in Transportation Engineering under the supervision of Dr. Salvador Hernandez. As a Research Assistant in the TranSAL research group, his work focuses on crash injury severity, human factors, freight network resiliency, transportation planning, and the use of big data in transportation safety. His broader research interests include network modelling and simulation, statistical econometrics, discrete choice modelling, intelligent transportation systems, and the application of machine learning, deep learning, and artificial intelligence in transportation, with particular emphasis on spatial econometrics and telematics data.

In addition to his research, Rakan has gained more than 400 hours of teaching experience as a Teaching Assistant in several engineering courses. These include Introduction to Highway Engineering (CE392), Plane Surveying (CEM263), Surveying Theory (CE361), and Statics (ENGR211), where he supported students through lectures, laboratory sessions, recitations, and problem-solving activities. He is committed to advancing transportation engineering through innovative research and education, and he is also a member of ASCE and ITE, as well as a peer reviewer for distinguished transportation journals such as Accident Analysis & Prevention and Transportation Research Record.